{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SAd865lNzZpT"
   },
   "source": [
    "#  <span style=\"color:orange\">Regression Tutorial (REG101) - Level Beginner</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yXN8UznszZpc"
   },
   "source": [
    "**Date Updated: Feb 25, 2020**\n",
    "\n",
    "# 1.0  Tutorial Objective\n",
    "Welcome to Regression Tutorial (REG101) - Level Beginner. This tutorial assumes that you are new to PyCaret and looking to get started with Regression using the `pycaret.regression` Module.\n",
    "\n",
    "In this tutorial we will learn:\n",
    "\n",
    "\n",
    "* **Getting Data:**  How to import data from PyCaret repository\n",
    "* **Setting up Environment:**  How to setup an experiment in PyCaret and get started with building regression models\n",
    "* **Create Model:**  How to create a model, perform cross validation and evaluate regression metrics\n",
    "* **Tune Model:**  How to automatically tune the hyperparameters of a regression model\n",
    "* **Plot Model:**  How to analyze model performance using various plots\n",
    "* **Finalize Model:** How to finalize the best model at the end of the experiment\n",
    "* **Predict Model:**  How to make prediction on new / unseen data\n",
    "* **Save / Load Model:**  How to save / load a model for future use\n",
    "\n",
    "Read Time : Approx. 30 Minutes\n",
    "\n",
    "\n",
    "## 1.1 Installing PyCaret\n",
    "The first step to get started with PyCaret is to install PyCaret. Installation is easy and will only take a few minutes. Follow the instructions below:\n",
    "\n",
    "#### Installing PyCaret in Local Jupyter Notebook\n",
    "`pip install pycaret`  <br />\n",
    "\n",
    "#### Installing PyCaret on Google Colab or Azure Notebooks\n",
    "`!pip install pycaret`\n",
    "\n",
    "\n",
    "## 1.2 Pre-Requisites\n",
    "- Python 3.x\n",
    "- Latest version of pycaret\n",
    "- Internet connection to load data from pycaret's repository\n",
    "- Basic Knowledge of Regression\n",
    "\n",
    "## 1.3 For Google Colab Users:\n",
    "If you are running this notebook on Google colab, run the following code at top of your notebook to display interactive visuals.<br/>\n",
    "<br/>\n",
    "`from pycaret.utils import enable_colab` <br/>\n",
    "`enable_colab()`\n",
    "\n",
    "## 1.4 See also:\n",
    "- __[Regression Tutorial (REG102) - Level Intermediate](https://github.com/pycaret/pycaret/blob/master/Tutorials/Regression%20Tutorial%20Level%20Intermediate%20-%20REG102.ipynb)__\n",
    "- __[Regression Tutorial (REG103) - Level Expert](https://github.com/pycaret/pycaret/blob/master/Tutorials/Regression%20Tutorial%20Level%20Expert%20-%20REG103.ipynb)__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HuEUiXXhzZpi"
   },
   "source": [
    "# 2.0 What is Regression?\n",
    "\n",
    "Regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome variable', or 'target') and one or more independent variables (often called 'features', 'predictors', or 'covariates'). The objective of regression in machine learning is to predict continuous values such as sales amount, quantity, temperature etc.\n",
    "\n",
    "__[Learn More about Regression](https://hbr.org/2015/11/a-refresher-on-regression-analysis)__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xnEk7n5ZzZpm"
   },
   "source": [
    "# 3.0 Overview of the Regression Module in PyCaret\n",
    "PyCaret's Regression module (`pycaret.regression`) is a supervised machine learning module which is used for predicting continuous values / outcomes using various techniques and algorithms. Regression can be used for predicting values / outcomes such as sales, units sold, temperature or any number which is continuous.\n",
    "\n",
    "PyCaret's regression module has over 25 algorithms and 10 plots to analyze the performance of models. Be it hyper-parameter tuning, ensembling or advanced techniques like stacking, PyCaret's regression module has it all."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uN95Uqo6zZpq"
   },
   "source": [
    "# 4.0 Dataset for the Tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Guj8GFIJzZpu"
   },
   "source": [
    "For this tutorial we will use a dataset based on a case study called **\"Sarah Gets a Diamond\"**. This case was presented in the first year decision analysis course at Darden School of Business (University of Virginia). The basis for the data is a case regarding a hopeless romantic MBA student choosing the right diamond for his bride-to-be, Sarah. The data contains 6000 records for training. Short descriptions of each column are as follows:\n",
    "\n",
    "- **ID:** Uniquely identifies each observation (diamond)\n",
    "- **Carat Weight:** The weight of the diamond in metric carats. One carat is equal to 0.2 grams, roughly the same weight as a paperclip\n",
    "- **Cut:** One of five values indicating the cut of the diamond in the following order of desirability (Signature-Ideal, Ideal, Very Good, Good, Fair)\n",
    "- **Color:** One of six values indicating the diamond's color in the following order of desirability (D, E, F - Colorless, G, H, I - Near colorless)\n",
    "- **Clarity:** One of seven values indicating the diamond's clarity in the following order of desirability (F - Flawless, IF - Internally Flawless, VVS1 or VVS2 - Very, Very Slightly Included, or VS1 or VS2 - Very Slightly Included, SI1 - Slightly Included)\n",
    "- **Polish:** One of four values indicating the diamond's polish (ID - Ideal, EX - Excellent, VG - Very Good, G - Good)\n",
    "- **Symmetry:** One of four values indicating the diamond's symmetry (ID - Ideal, EX - Excellent, VG - Very Good, G - Good)\n",
    "- **Report:** One of of two values \"AGSL\" or \"GIA\" indicating which grading agency reported the qualities of the diamond qualities\n",
    "- **Price:** The amount in USD that the diamond is valued `Target Column`\n",
    "\n",
    "\n",
    "#### Dataset Acknowledgement:\n",
    "This case was prepared by Greg Mills (MBA ’07) under the supervision of Phillip E. Pfeifer, Alumni Research Professor of Business Administration. Copyright (c) 2007 by the University of Virginia Darden School Foundation, Charlottesville, VA. All rights reserved.\n",
    "\n",
    "The original dataset and description can be __[found here.](https://github.com/DardenDSC/sarah-gets-a-diamond)__ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wwUzzm1YzZpz"
   },
   "source": [
    "# 5.0 Getting the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PFCSZ_NKzZp3"
   },
   "source": [
    "You can download the data from the original source __[found here](https://github.com/DardenDSC/sarah-gets-a-diamond)__ and load it using pandas __[(Learn How)](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html)__ or you can use PyCaret's data respository to load the data using the `get_data()` function (This will require internet connection)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:12:42.891205Z",
     "start_time": "2020-04-23T13:12:42.168861Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 191
    },
    "colab_type": "code",
    "id": "H6qS5U--zZp7",
    "outputId": "2a11a81c-7e67-425a-a3ef-091d2c9fbd30"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Carat Weight</th>\n",
       "      <th>Cut</th>\n",
       "      <th>Color</th>\n",
       "      <th>Clarity</th>\n",
       "      <th>Polish</th>\n",
       "      <th>Symmetry</th>\n",
       "      <th>Report</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.10</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>H</td>\n",
       "      <td>SI1</td>\n",
       "      <td>VG</td>\n",
       "      <td>EX</td>\n",
       "      <td>GIA</td>\n",
       "      <td>5169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.83</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>H</td>\n",
       "      <td>VS1</td>\n",
       "      <td>ID</td>\n",
       "      <td>ID</td>\n",
       "      <td>AGSL</td>\n",
       "      <td>3470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.85</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>H</td>\n",
       "      <td>SI1</td>\n",
       "      <td>EX</td>\n",
       "      <td>EX</td>\n",
       "      <td>GIA</td>\n",
       "      <td>3183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.91</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>VG</td>\n",
       "      <td>VG</td>\n",
       "      <td>GIA</td>\n",
       "      <td>4370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.83</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>G</td>\n",
       "      <td>SI1</td>\n",
       "      <td>EX</td>\n",
       "      <td>EX</td>\n",
       "      <td>GIA</td>\n",
       "      <td>3171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Carat Weight    Cut Color Clarity Polish Symmetry Report  Price\n",
       "0          1.10  Ideal     H     SI1     VG       EX    GIA   5169\n",
       "1          0.83  Ideal     H     VS1     ID       ID   AGSL   3470\n",
       "2          0.85  Ideal     H     SI1     EX       EX    GIA   3183\n",
       "3          0.91  Ideal     E     SI1     VG       VG    GIA   4370\n",
       "4          0.83  Ideal     G     SI1     EX       EX    GIA   3171"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycaret.datasets import get_data\n",
    "dataset = get_data('diamond')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:12:45.180048Z",
     "start_time": "2020-04-23T13:12:45.176712Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "D5PerU66zZqK",
    "outputId": "2fdd6ab8-7d68-4cc4-81a7-0cb82ed70799"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6000, 8)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the shape of data\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7eWmeLvYzZqY"
   },
   "source": [
    "In order to demonstrate the `predict_model()` function on unseen data, a sample of 600 records has been withheld from the original dataset to be used for predictions. This should not be confused with a train/test split as this particular split is performed to simulate a real life scenario. Another way to think about this is that these 600 records are not available at the time when the machine learning experiment was performed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:13:11.922396Z",
     "start_time": "2020-04-23T13:13:11.912627Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "R4K9F7BXzZqc",
    "outputId": "22b1c4e7-a1e1-48d2-8ddc-907e716d5b53"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data for Modeling: (5400, 8)\n",
      "Unseen Data For Predictions: (600, 8)\n"
     ]
    }
   ],
   "source": [
    "data = dataset.sample(frac=0.9, random_state=786).reset_index(drop=True)\n",
    "data_unseen = dataset.drop(data.index).reset_index(drop=True)\n",
    "\n",
    "print('Data for Modeling: ' + str(data.shape))\n",
    "print('Unseen Data For Predictions: ' + str(data_unseen.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DxnJV14BzZqq"
   },
   "source": [
    "# 6.0 Setting up Environment in PyCaret"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "15-blMPOzZqw"
   },
   "source": [
    "The `setup()` function initializes the environment in pycaret and creates the transformation pipeline to prepare the data for modeling and deployment. `setup()` must be called before executing any other function in pycaret. It takes two mandatory parameters: a pandas dataframe and the name of the target column. All other parameters are optional and are used to customize the pre-processing pipeline (we will see them in later tutorials).\n",
    "\n",
    "When `setup()` is executed, PyCaret's inference algorithm will automatically infer the data types for all features based on certain properties. The data type should be inferred correctly but this is not always the case. To account for this, PyCaret displays a table containing the features and their inferred data types after `setup()` is executed. If all of the data types are correctly identified `enter` can be pressed to continue or `quit` can be typed to end the expriment. Ensuring that the data types are correct is of fundamental importance in PyCaret as it automatically performs a few pre-processing tasks which are imperative to any machine learning experiment. These tasks are performed differently for each data type which means it is very important for them to be correctly configured.\n",
    "\n",
    "In later tutorials we will learn how to overwrite PyCaret's inferred data type using the `numeric_features` and `categorical_features` parameters in `setup()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:15:53.461915Z",
     "start_time": "2020-04-23T13:15:53.457769Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "LgT-XURDzZqz"
   },
   "outputs": [],
   "source": [
    "from pycaret.regression import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:16:02.144917Z",
     "start_time": "2020-04-23T13:15:57.427822Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 803
    },
    "colab_type": "code",
    "id": "7V2FN4KQzZrA",
    "outputId": "43d8d23d-ef08-438a-8cc3-ba78e9773aca",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "Setup Succesfully Completed!\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_94693bbe_8564_11ea_8331_049226d935bb\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Description</th>        <th class=\"col_heading level0 col1\" >Value</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow0_col1\" class=\"data row0 col1\" >123</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow1_col0\" class=\"data row1 col0\" >Transform Target </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow1_col1\" class=\"data row1 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow2_col0\" class=\"data row2 col0\" >Transform Target Method</td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow2_col1\" class=\"data row2 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow3_col0\" class=\"data row3 col0\" >Original Data</td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow3_col1\" class=\"data row3 col1\" >(5400, 8)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow4_col0\" class=\"data row4 col0\" >Missing Values </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow4_col1\" class=\"data row4 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow5_col0\" class=\"data row5 col0\" >Numeric Features </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow5_col1\" class=\"data row5 col1\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow6_col0\" class=\"data row6 col0\" >Categorical Features </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow6_col1\" class=\"data row6 col1\" >6</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow7_col0\" class=\"data row7 col0\" >Ordinal Features </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow7_col1\" class=\"data row7 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow8_col0\" class=\"data row8 col0\" >High Cardinality Features </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow8_col1\" class=\"data row8 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow9_col0\" class=\"data row9 col0\" >High Cardinality Method </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow9_col1\" class=\"data row9 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow10_col0\" class=\"data row10 col0\" >Sampled Data</td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow10_col1\" class=\"data row10 col1\" >(5400, 8)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow11_col0\" class=\"data row11 col0\" >Transformed Train Set</td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow11_col1\" class=\"data row11 col1\" >(3779, 28)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow12_col0\" class=\"data row12 col0\" >Transformed Test Set</td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow12_col1\" class=\"data row12 col1\" >(1621, 28)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow13_col0\" class=\"data row13 col0\" >Numeric Imputer </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow13_col1\" class=\"data row13 col1\" >mean</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow14_col0\" class=\"data row14 col0\" >Categorical Imputer </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow14_col1\" class=\"data row14 col1\" >constant</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow15_col0\" class=\"data row15 col0\" >Normalize </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow15_col1\" class=\"data row15 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow16_col0\" class=\"data row16 col0\" >Normalize Method </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow16_col1\" class=\"data row16 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow17_col0\" class=\"data row17 col0\" >Transformation </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow17_col1\" class=\"data row17 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow18_col0\" class=\"data row18 col0\" >Transformation Method </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow18_col1\" class=\"data row18 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow19_col0\" class=\"data row19 col0\" >PCA </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow19_col1\" class=\"data row19 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow20_col0\" class=\"data row20 col0\" >PCA Method </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow20_col1\" class=\"data row20 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow21_col0\" class=\"data row21 col0\" >PCA Components </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow21_col1\" class=\"data row21 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow22_col0\" class=\"data row22 col0\" >Ignore Low Variance </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow22_col1\" class=\"data row22 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow23_col0\" class=\"data row23 col0\" >Combine Rare Levels </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow23_col1\" class=\"data row23 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow24_col0\" class=\"data row24 col0\" >Rare Level Threshold </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow24_col1\" class=\"data row24 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow25_col0\" class=\"data row25 col0\" >Numeric Binning </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow25_col1\" class=\"data row25 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow26_col0\" class=\"data row26 col0\" >Remove Outliers </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow26_col1\" class=\"data row26 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow27_col0\" class=\"data row27 col0\" >Outliers Threshold </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow27_col1\" class=\"data row27 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow28_col0\" class=\"data row28 col0\" >Remove Multicollinearity </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow28_col1\" class=\"data row28 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow29_col0\" class=\"data row29 col0\" >Multicollinearity Threshold </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow29_col1\" class=\"data row29 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow30_col0\" class=\"data row30 col0\" >Clustering </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow30_col1\" class=\"data row30 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow31_col0\" class=\"data row31 col0\" >Clustering Iteration </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow31_col1\" class=\"data row31 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow32_col0\" class=\"data row32 col0\" >Polynomial Features </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow32_col1\" class=\"data row32 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow33_col0\" class=\"data row33 col0\" >Polynomial Degree </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow33_col1\" class=\"data row33 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow34_col0\" class=\"data row34 col0\" >Trignometry Features </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow34_col1\" class=\"data row34 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow35_col0\" class=\"data row35 col0\" >Polynomial Threshold </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow35_col1\" class=\"data row35 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow36_col0\" class=\"data row36 col0\" >Group Features </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow36_col1\" class=\"data row36 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow37_col0\" class=\"data row37 col0\" >Feature Selection </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow37_col1\" class=\"data row37 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow38_col0\" class=\"data row38 col0\" >Features Selection Threshold </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow38_col1\" class=\"data row38 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow39_col0\" class=\"data row39 col0\" >Feature Interaction </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow39_col1\" class=\"data row39 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow40_col0\" class=\"data row40 col0\" >Feature Ratio </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow40_col1\" class=\"data row40 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_94693bbe_8564_11ea_8331_049226d935bblevel0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow41_col0\" class=\"data row41 col0\" >Interaction Threshold </td>\n",
       "                        <td id=\"T_94693bbe_8564_11ea_8331_049226d935bbrow41_col1\" class=\"data row41 col1\" >None</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f9fefe9b1c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "exp_reg101 = setup(data = data, target = 'Price', session_id=123) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nWBypX32zZrP"
   },
   "source": [
    "Once the setup has been succesfully executed it prints the information grid which contains several important pieces of information. Most of the information is related to the pre-processing pipeline which is constructed when `setup()` is executed. The majority of these features are out of scope for the purposes of this tutorial. However, a few important things to note at this stage include:\n",
    "\n",
    "- **session_id :**  A pseudo-random number distributed as a seed in all functions for later reproducibility. If no `session_id` is passed, a random number is automatically generated that is distributed to all functions. In this experiment, the `session_id` is set as `123` for later reproducibility.<br/>\n",
    "<br/>\n",
    "- **Original Data :**  Displays the original shape of dataset. In this experiment (5400, 8) means 5400 samples and 8 features including the target column. <br/>\n",
    "<br/>\n",
    "- **Missing Values :**  When there are missing values in the original data, this will show as True. For this experiment there are no missing values in the dataset.<br/>\n",
    "<br/>\n",
    "- **Numeric Features :**  Number of features inferred as numeric. In this dataset, 1 out of 8 features are inferred as numeric. <br/>\n",
    "<br/>\n",
    "- **Categorical Features :**  Number of features inferred as categorical. In this dataset, 6 out of 8 features are inferred as categorical. <br/>\n",
    "<br/>\n",
    "- **Transformed Train Set :** Displays the shape of the transformed training set. Notice that the original shape of (5400, 8) is transformed into (3779, 28) for the transformed train set. The number of features has increased from 8 from 28 due to categorical encoding <br/>\n",
    "<br/>\n",
    "- **Transformed Test Set :** Displays the shape of transformed test/hold-out set. There are 1621 samples in test/hold-out set. This split is based on the default value of 70/30 that can be changed using `train_size` parameter in setup. <br/>\n",
    "\n",
    "Notice how a few tasks that are imperative to perform modeling are automatically handled, such as missing value imputation (in this case there are no missing values in training data, but we still need imputers for unseen data), categorical encoding etc. Most of the parameters in `setup()` are optional and used for customizing the pre-processing pipeline. These parameters are out of scope for this tutorial but as you progress to the intermediate and expert levels, we will cover them in much greater detail.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xBqHzabEzZrT"
   },
   "source": [
    "# 7.0 Comparing All Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QHiNl6UmzZrW"
   },
   "source": [
    "Comparing all models to evaluate performance is the recommended starting point for modeling once the setup is completed (unless you exactly know what kind of model you need, which is often not the case). This function trains all models in the model library and scores them using k-fold cross validation for metric evaluation. The output prints a score grid that shows average MAE, MSE, RMSE, R2, RMSLE and MAPE accross the folds (10 by default) of all the available models in the model library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:21:22.884291Z",
     "start_time": "2020-04-23T13:20:11.108031Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 421
    },
    "colab_type": "code",
    "id": "atJfMGD6zZrb",
    "outputId": "af936da0-cc93-4429-ef61-20d940e0aa4e",
    "scrolled": false
   },
   "outputs": [
    {
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       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_53961bce_8565_11ea_8331_049226d935bbrow21_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_53961bce_8565_11ea_8331_049226d935bbrow21_col4 {\n",
       "            text-align:  left;\n",
       "        }    #T_53961bce_8565_11ea_8331_049226d935bbrow21_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
       "        }</style><table id=\"T_53961bce_8565_11ea_8331_049226d935bb\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Model</th>        <th class=\"col_heading level0 col1\" >MAE</th>        <th class=\"col_heading level0 col2\" >MSE</th>        <th class=\"col_heading level0 col3\" >RMSE</th>        <th class=\"col_heading level0 col4\" >R2</th>        <th class=\"col_heading level0 col5\" >RMSLE</th>        <th class=\"col_heading level0 col6\" >MAPE</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_53961bce_8565_11ea_8331_049226d935bblevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow0_col0\" class=\"data row0 col0\" >CatBoost Regressor</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow0_col1\" class=\"data row0 col1\" >629.397100</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow0_col2\" class=\"data row0 col2\" >2079335.022600</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow0_col3\" class=\"data row0 col3\" >1376.306600</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow0_col4\" class=\"data row0 col4\" >0.980300</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow0_col5\" class=\"data row0 col5\" >0.067600</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow0_col6\" class=\"data row0 col6\" >0.049700</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53961bce_8565_11ea_8331_049226d935bblevel0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow1_col0\" class=\"data row1 col0\" >Extra Trees Regressor</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow1_col1\" class=\"data row1 col1\" >762.011800</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow1_col2\" class=\"data row1 col2\" >2763999.158500</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow1_col3\" class=\"data row1 col3\" >1612.241000</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow1_col4\" class=\"data row1 col4\" >0.972900</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow1_col5\" class=\"data row1 col5\" >0.081700</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow1_col6\" class=\"data row1 col6\" >0.060700</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53961bce_8565_11ea_8331_049226d935bblevel0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow2_col0\" class=\"data row2 col0\" >Random Forest</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow2_col1\" class=\"data row2 col1\" >760.630400</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow2_col2\" class=\"data row2 col2\" >2929683.186000</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow2_col3\" class=\"data row2 col3\" >1663.014800</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow2_col4\" class=\"data row2 col4\" >0.971400</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow2_col5\" class=\"data row2 col5\" >0.081800</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow2_col6\" class=\"data row2 col6\" >0.059700</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53961bce_8565_11ea_8331_049226d935bblevel0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow3_col0\" class=\"data row3 col0\" >Light Gradient Boosting Machine</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow3_col1\" class=\"data row3 col1\" >752.235700</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow3_col2\" class=\"data row3 col2\" >3055569.400400</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow3_col3\" class=\"data row3 col3\" >1687.777000</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow3_col4\" class=\"data row3 col4\" >0.971100</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow3_col5\" class=\"data row3 col5\" >0.077300</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow3_col6\" class=\"data row3 col6\" >0.056700</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53961bce_8565_11ea_8331_049226d935bblevel0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow4_col0\" class=\"data row4 col0\" >Extreme Gradient Boosting</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow4_col1\" class=\"data row4 col1\" >932.797400</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow4_col2\" class=\"data row4 col2\" >3534049.448500</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow4_col3\" class=\"data row4 col3\" >1855.329900</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow4_col4\" class=\"data row4 col4\" >0.965200</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow4_col5\" class=\"data row4 col5\" >0.106100</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow4_col6\" class=\"data row4 col6\" >0.080100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53961bce_8565_11ea_8331_049226d935bblevel0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow5_col0\" class=\"data row5 col0\" >Gradient Boosting Regressor</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow5_col1\" class=\"data row5 col1\" >920.291300</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow5_col2\" class=\"data row5 col2\" >3764303.925200</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow5_col3\" class=\"data row5 col3\" >1901.179300</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow5_col4\" class=\"data row5 col4\" >0.963300</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow5_col5\" class=\"data row5 col5\" >0.102400</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow5_col6\" class=\"data row5 col6\" >0.077000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53961bce_8565_11ea_8331_049226d935bblevel0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow6_col0\" class=\"data row6 col0\" >Decision Tree</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow6_col1\" class=\"data row6 col1\" >1003.123700</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow6_col2\" class=\"data row6 col2\" >5305620.337900</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow6_col3\" class=\"data row6 col3\" >2228.727100</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow6_col4\" class=\"data row6 col4\" >0.947600</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow6_col5\" class=\"data row6 col5\" >0.108300</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow6_col6\" class=\"data row6 col6\" >0.077500</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53961bce_8565_11ea_8331_049226d935bblevel0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow7_col0\" class=\"data row7 col0\" >Ridge Regression</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow7_col1\" class=\"data row7 col1\" >2413.569800</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow7_col2\" class=\"data row7 col2\" >14120482.759200</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow7_col3\" class=\"data row7 col3\" >3726.163100</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow7_col4\" class=\"data row7 col4\" >0.862100</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow7_col5\" class=\"data row7 col5\" >0.668900</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow7_col6\" class=\"data row7 col6\" >0.287500</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53961bce_8565_11ea_8331_049226d935bblevel0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow8_col0\" class=\"data row8 col0\" >Lasso Regression</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow8_col1\" class=\"data row8 col1\" >2412.192300</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow8_col2\" class=\"data row8 col2\" >14246796.409100</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow8_col3\" class=\"data row8 col3\" >3744.230300</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow8_col4\" class=\"data row8 col4\" >0.860800</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow8_col5\" class=\"data row8 col5\" >0.676700</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow8_col6\" class=\"data row8 col6\" >0.286600</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53961bce_8565_11ea_8331_049226d935bblevel0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow9_col0\" class=\"data row9 col0\" >Least Angle Regression</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow9_col1\" class=\"data row9 col1\" >2414.296000</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow9_col2\" class=\"data row9 col2\" >14243229.337400</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow9_col3\" class=\"data row9 col3\" >3744.165400</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow9_col4\" class=\"data row9 col4\" >0.860700</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow9_col5\" class=\"data row9 col5\" >0.668200</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow9_col6\" class=\"data row9 col6\" >0.287100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53961bce_8565_11ea_8331_049226d935bblevel0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow10_col0\" class=\"data row10 col0\" >Lasso Least Angle Regression</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow10_col1\" class=\"data row10 col1\" >2355.614700</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow10_col2\" class=\"data row10 col2\" >14272022.078300</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow10_col3\" class=\"data row10 col3\" >3745.309600</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow10_col4\" class=\"data row10 col4\" >0.860700</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow10_col5\" class=\"data row10 col5\" >0.639100</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow10_col6\" class=\"data row10 col6\" >0.272800</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53961bce_8565_11ea_8331_049226d935bblevel0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow11_col0\" class=\"data row11 col0\" >Bayesian Ridge</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow11_col1\" class=\"data row11 col1\" >2415.803100</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow11_col2\" class=\"data row11 col2\" >14270771.684400</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow11_col3\" class=\"data row11 col3\" >3746.995000</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow11_col4\" class=\"data row11 col4\" >0.860600</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow11_col5\" class=\"data row11 col5\" >0.669600</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow11_col6\" class=\"data row11 col6\" >0.287300</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53961bce_8565_11ea_8331_049226d935bblevel0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow12_col0\" class=\"data row12 col0\" >Linear Regression</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow12_col1\" class=\"data row12 col1\" >2510.045800</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow12_col2\" class=\"data row12 col2\" >14943947.987200</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow12_col3\" class=\"data row12 col3\" >3831.741100</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow12_col4\" class=\"data row12 col4\" >0.854100</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow12_col5\" class=\"data row12 col5\" >0.721500</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow12_col6\" class=\"data row12 col6\" >0.300000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53961bce_8565_11ea_8331_049226d935bblevel0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow13_col0\" class=\"data row13 col0\" >TheilSen Regressor</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow13_col1\" class=\"data row13 col1\" >2130.775600</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow13_col2\" class=\"data row13 col2\" >16027816.207500</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow13_col3\" class=\"data row13 col3\" >3942.550400</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow13_col4\" class=\"data row13 col4\" >0.845900</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow13_col5\" class=\"data row13 col5\" >0.481400</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow13_col6\" class=\"data row13 col6\" >0.217300</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53961bce_8565_11ea_8331_049226d935bblevel0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow14_col0\" class=\"data row14 col0\" >Huber Regressor</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow14_col1\" class=\"data row14 col1\" >1936.014600</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow14_col2\" class=\"data row14 col2\" >18583886.591800</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow14_col3\" class=\"data row14 col3\" >4251.333400</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow14_col4\" class=\"data row14 col4\" >0.821000</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow14_col5\" class=\"data row14 col5\" >0.433200</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow14_col6\" class=\"data row14 col6\" >0.165700</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53961bce_8565_11ea_8331_049226d935bblevel0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow15_col0\" class=\"data row15 col0\" >Passive Aggressive Regressor</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow15_col1\" class=\"data row15 col1\" >1944.163400</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow15_col2\" class=\"data row15 col2\" >19955672.245900</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow15_col3\" class=\"data row15 col3\" >4400.213200</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow15_col4\" class=\"data row15 col4\" >0.808300</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow15_col5\" class=\"data row15 col5\" >0.431700</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow15_col6\" class=\"data row15 col6\" >0.159400</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53961bce_8565_11ea_8331_049226d935bblevel0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow16_col0\" class=\"data row16 col0\" >Orthogonal Matching Pursuit</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow16_col1\" class=\"data row16 col1\" >2792.731200</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow16_col2\" class=\"data row16 col2\" >23728654.526700</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow16_col3\" class=\"data row16 col3\" >4829.317100</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow16_col4\" class=\"data row16 col4\" >0.767800</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow16_col5\" class=\"data row16 col5\" >0.581800</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow16_col6\" class=\"data row16 col6\" >0.265400</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53961bce_8565_11ea_8331_049226d935bblevel0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow17_col0\" class=\"data row17 col0\" >AdaBoost Regressor</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow17_col1\" class=\"data row17 col1\" >4232.221700</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow17_col2\" class=\"data row17 col2\" >25201423.070300</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow17_col3\" class=\"data row17 col3\" >5012.417500</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow17_col4\" class=\"data row17 col4\" >0.746700</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow17_col5\" class=\"data row17 col5\" >0.510200</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow17_col6\" class=\"data row17 col6\" >0.597000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53961bce_8565_11ea_8331_049226d935bblevel0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow18_col0\" class=\"data row18 col0\" >K Neighbors Regressor</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow18_col1\" class=\"data row18 col1\" >2967.048300</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow18_col2\" class=\"data row18 col2\" >29624929.841300</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow18_col3\" class=\"data row18 col3\" >5421.441600</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow18_col4\" class=\"data row18 col4\" >0.705100</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow18_col5\" class=\"data row18 col5\" >0.366300</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow18_col6\" class=\"data row18 col6\" >0.272800</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53961bce_8565_11ea_8331_049226d935bblevel0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow19_col0\" class=\"data row19 col0\" >Elastic Net</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow19_col1\" class=\"data row19 col1\" >5029.591300</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow19_col2\" class=\"data row19 col2\" >56399794.945300</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow19_col3\" class=\"data row19 col3\" >7467.659700</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow19_col4\" class=\"data row19 col4\" >0.447200</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow19_col5\" class=\"data row19 col5\" >0.536900</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow19_col6\" class=\"data row19 col6\" >0.584500</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53961bce_8565_11ea_8331_049226d935bblevel0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow20_col0\" class=\"data row20 col0\" >Support Vector Machine</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow20_col1\" class=\"data row20 col1\" >6438.092600</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow20_col2\" class=\"data row20 col2\" >115315574.847600</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow20_col3\" class=\"data row20 col3\" >10710.418000</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow20_col4\" class=\"data row20 col4\" >-0.142200</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow20_col5\" class=\"data row20 col5\" >0.713700</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow20_col6\" class=\"data row20 col6\" >0.528100</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_53961bce_8565_11ea_8331_049226d935bblevel0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow21_col0\" class=\"data row21 col0\" >Random Sample Consensus</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow21_col1\" class=\"data row21 col1\" >10048906754800.681641</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow21_col2\" class=\"data row21 col2\" >381706391779875422496467451904.000000</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow21_col3\" class=\"data row21 col3\" >195373076905165.187500</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow21_col4\" class=\"data row21 col4\" >-3257764188583802437632.000000</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow21_col5\" class=\"data row21 col5\" >0.572500</td>\n",
       "                        <td id=\"T_53961bce_8565_11ea_8331_049226d935bbrow21_col6\" class=\"data row21 col6\" >104141303.222800</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f9fe96e8f40>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_models()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "epD0BEVyzZrr"
   },
   "source": [
    "Two simple words of code ***(not even a line)*** have created over 22 models using 10 fold cross validation and evaluated the 6 most commonly used regression metrics (MAE, MSE, RMSE, R2, RMSLE and MAPE). The score grid printed above highlights the highest performing metric for comparison purposes only. The grid by default is sorted using `R2` (highest to lowest) which can be changed by passing `sort` parameter. For example `compare_models(sort = 'RMSLE')` will sort the grid by RMSLE (lower to higher since lower is better). If you want to change the fold parameter from the default value of `10` to a different value then you can use the `fold` parameter. For example `compare_models(fold = 5)` will compare all models on 5 fold cross validation. Reducing the number of folds will improve the training time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZzpBazV1zZrx"
   },
   "source": [
    "# 8.0 Create a Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IPqPRp5OzZr1"
   },
   "source": [
    "While `compare_models()` is a powerful function and often a starting point in any experiment, it does not return any trained models. PyCaret's recommended experiment workflow is to use `compare_models()` right after setup to evaluate top performing models and finalize a few candidates for continued experimentation. As such, the function that actually allows to you create a model is unimaginatively called `create_model()`. This function creates a model and scores it using stratified cross validation. Similar to `compare_models()`, the output prints a score grid that shows MAE, MSE, RMSE, R2, RMSLE and MAPE by fold. \n",
    "\n",
    "For the remaining part of this tutorial, we will work with the below models as our candidate models. The selections are for illustration purposes only and do not necessarily mean they are the top performing or ideal for this type of data.\n",
    "\n",
    "- AdaBoost Regressor ('ada')\n",
    "- Light Gradient Boosting Machine ('lightgbm') \n",
    "- Decision Tree\t ('dt')\n",
    "\n",
    "There are 25 regressors available in the model library of PyCaret. Please view the `create_model()` docstring for the list of all available models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wxKHHQcbzZr5"
   },
   "source": [
    "### 8.1 AdaBoost Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:22:30.940588Z",
     "start_time": "2020-04-23T13:22:28.992149Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "id": "-NVGDCR3zZr8",
    "outputId": "06f5fc68-d2a5-4b59-fea2-3661ea1cb29d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4101.8809</td>\n",
       "      <td>2.301383e+07</td>\n",
       "      <td>4797.2732</td>\n",
       "      <td>0.7473</td>\n",
       "      <td>0.4758</td>\n",
       "      <td>0.5470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4251.5693</td>\n",
       "      <td>2.929675e+07</td>\n",
       "      <td>5412.6474</td>\n",
       "      <td>0.7755</td>\n",
       "      <td>0.4940</td>\n",
       "      <td>0.5702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4047.8474</td>\n",
       "      <td>2.229166e+07</td>\n",
       "      <td>4721.4045</td>\n",
       "      <td>0.7955</td>\n",
       "      <td>0.5068</td>\n",
       "      <td>0.5871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4298.3867</td>\n",
       "      <td>2.348278e+07</td>\n",
       "      <td>4845.9038</td>\n",
       "      <td>0.7409</td>\n",
       "      <td>0.5089</td>\n",
       "      <td>0.5960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3888.5584</td>\n",
       "      <td>2.446181e+07</td>\n",
       "      <td>4945.8880</td>\n",
       "      <td>0.6949</td>\n",
       "      <td>0.4764</td>\n",
       "      <td>0.5461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4566.4889</td>\n",
       "      <td>2.973391e+07</td>\n",
       "      <td>5452.8813</td>\n",
       "      <td>0.7462</td>\n",
       "      <td>0.5462</td>\n",
       "      <td>0.6598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4628.7271</td>\n",
       "      <td>2.784109e+07</td>\n",
       "      <td>5276.4659</td>\n",
       "      <td>0.7384</td>\n",
       "      <td>0.5549</td>\n",
       "      <td>0.6676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4316.4317</td>\n",
       "      <td>2.597975e+07</td>\n",
       "      <td>5097.0336</td>\n",
       "      <td>0.6715</td>\n",
       "      <td>0.5034</td>\n",
       "      <td>0.5858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3931.2163</td>\n",
       "      <td>2.109707e+07</td>\n",
       "      <td>4593.1549</td>\n",
       "      <td>0.7928</td>\n",
       "      <td>0.4858</td>\n",
       "      <td>0.5513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4291.1097</td>\n",
       "      <td>2.481557e+07</td>\n",
       "      <td>4981.5225</td>\n",
       "      <td>0.7637</td>\n",
       "      <td>0.5495</td>\n",
       "      <td>0.6592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>4232.2217</td>\n",
       "      <td>2.520142e+07</td>\n",
       "      <td>5012.4175</td>\n",
       "      <td>0.7467</td>\n",
       "      <td>0.5102</td>\n",
       "      <td>0.5970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>233.2282</td>\n",
       "      <td>2.804219e+06</td>\n",
       "      <td>277.6577</td>\n",
       "      <td>0.0375</td>\n",
       "      <td>0.0284</td>\n",
       "      <td>0.0457</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            MAE           MSE       RMSE      R2   RMSLE    MAPE\n",
       "0     4101.8809  2.301383e+07  4797.2732  0.7473  0.4758  0.5470\n",
       "1     4251.5693  2.929675e+07  5412.6474  0.7755  0.4940  0.5702\n",
       "2     4047.8474  2.229166e+07  4721.4045  0.7955  0.5068  0.5871\n",
       "3     4298.3867  2.348278e+07  4845.9038  0.7409  0.5089  0.5960\n",
       "4     3888.5584  2.446181e+07  4945.8880  0.6949  0.4764  0.5461\n",
       "5     4566.4889  2.973391e+07  5452.8813  0.7462  0.5462  0.6598\n",
       "6     4628.7271  2.784109e+07  5276.4659  0.7384  0.5549  0.6676\n",
       "7     4316.4317  2.597975e+07  5097.0336  0.6715  0.5034  0.5858\n",
       "8     3931.2163  2.109707e+07  4593.1549  0.7928  0.4858  0.5513\n",
       "9     4291.1097  2.481557e+07  4981.5225  0.7637  0.5495  0.6592\n",
       "Mean  4232.2217  2.520142e+07  5012.4175  0.7467  0.5102  0.5970\n",
       "SD     233.2282  2.804219e+06   277.6577  0.0375  0.0284  0.0457"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ada = create_model('ada')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:22:38.337909Z",
     "start_time": "2020-04-23T13:22:38.335103Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "NHL2zciizZsI",
    "outputId": "d606ad03-ecd5-487b-b205-bfc7a841f3a5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoostRegressor(base_estimator=None, learning_rate=1.0, loss='linear',\n",
      "                  n_estimators=50, random_state=123)\n"
     ]
    }
   ],
   "source": [
    "#trained model object is stored in the variable 'ada'. \n",
    "print(ada)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "T-dvDHxCzZsU"
   },
   "source": [
    "### 8.2 Light Gradient Boosting Machine "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:22:46.292921Z",
     "start_time": "2020-04-23T13:22:43.713857Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "id": "NC7OVDVrzZsX",
    "outputId": "a5abc702-d270-4134-892a-e9ecf82bdebb"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>625.1813</td>\n",
       "      <td>1.051763e+06</td>\n",
       "      <td>1025.5550</td>\n",
       "      <td>0.9885</td>\n",
       "      <td>0.0715</td>\n",
       "      <td>0.0526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>797.6185</td>\n",
       "      <td>5.638866e+06</td>\n",
       "      <td>2374.6297</td>\n",
       "      <td>0.9568</td>\n",
       "      <td>0.0727</td>\n",
       "      <td>0.0537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>825.0215</td>\n",
       "      <td>3.318727e+06</td>\n",
       "      <td>1821.7374</td>\n",
       "      <td>0.9696</td>\n",
       "      <td>0.0857</td>\n",
       "      <td>0.0616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>720.3923</td>\n",
       "      <td>1.697211e+06</td>\n",
       "      <td>1302.7707</td>\n",
       "      <td>0.9813</td>\n",
       "      <td>0.0714</td>\n",
       "      <td>0.0554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>645.6800</td>\n",
       "      <td>1.799949e+06</td>\n",
       "      <td>1341.6218</td>\n",
       "      <td>0.9775</td>\n",
       "      <td>0.0745</td>\n",
       "      <td>0.0534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>830.7176</td>\n",
       "      <td>6.423604e+06</td>\n",
       "      <td>2534.4830</td>\n",
       "      <td>0.9452</td>\n",
       "      <td>0.0810</td>\n",
       "      <td>0.0567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>800.2611</td>\n",
       "      <td>3.355856e+06</td>\n",
       "      <td>1831.8996</td>\n",
       "      <td>0.9685</td>\n",
       "      <td>0.0793</td>\n",
       "      <td>0.0585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>714.3607</td>\n",
       "      <td>1.930223e+06</td>\n",
       "      <td>1389.3245</td>\n",
       "      <td>0.9756</td>\n",
       "      <td>0.0732</td>\n",
       "      <td>0.0556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>784.7648</td>\n",
       "      <td>2.211933e+06</td>\n",
       "      <td>1487.2569</td>\n",
       "      <td>0.9783</td>\n",
       "      <td>0.0766</td>\n",
       "      <td>0.0582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>778.3590</td>\n",
       "      <td>3.127561e+06</td>\n",
       "      <td>1768.4913</td>\n",
       "      <td>0.9702</td>\n",
       "      <td>0.0872</td>\n",
       "      <td>0.0609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>752.2357</td>\n",
       "      <td>3.055569e+06</td>\n",
       "      <td>1687.7770</td>\n",
       "      <td>0.9711</td>\n",
       "      <td>0.0773</td>\n",
       "      <td>0.0567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>68.9270</td>\n",
       "      <td>1.661228e+06</td>\n",
       "      <td>454.9486</td>\n",
       "      <td>0.0119</td>\n",
       "      <td>0.0055</td>\n",
       "      <td>0.0029</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           MAE           MSE       RMSE      R2   RMSLE    MAPE\n",
       "0     625.1813  1.051763e+06  1025.5550  0.9885  0.0715  0.0526\n",
       "1     797.6185  5.638866e+06  2374.6297  0.9568  0.0727  0.0537\n",
       "2     825.0215  3.318727e+06  1821.7374  0.9696  0.0857  0.0616\n",
       "3     720.3923  1.697211e+06  1302.7707  0.9813  0.0714  0.0554\n",
       "4     645.6800  1.799949e+06  1341.6218  0.9775  0.0745  0.0534\n",
       "5     830.7176  6.423604e+06  2534.4830  0.9452  0.0810  0.0567\n",
       "6     800.2611  3.355856e+06  1831.8996  0.9685  0.0793  0.0585\n",
       "7     714.3607  1.930223e+06  1389.3245  0.9756  0.0732  0.0556\n",
       "8     784.7648  2.211933e+06  1487.2569  0.9783  0.0766  0.0582\n",
       "9     778.3590  3.127561e+06  1768.4913  0.9702  0.0872  0.0609\n",
       "Mean  752.2357  3.055569e+06  1687.7770  0.9711  0.0773  0.0567\n",
       "SD     68.9270  1.661228e+06   454.9486  0.0119  0.0055  0.0029"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lightgbm = create_model('lightgbm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "j8DvIuOrzZsm"
   },
   "source": [
    "### 8.3 Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:22:50.364299Z",
     "start_time": "2020-04-23T13:22:50.002325Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "id": "1Y_Czm6xzZsr",
    "outputId": "df27ebb4-257b-440e-cd9b-de05ba0c0f35"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>859.1907</td>\n",
       "      <td>2.456840e+06</td>\n",
       "      <td>1567.4310</td>\n",
       "      <td>0.9730</td>\n",
       "      <td>0.1016</td>\n",
       "      <td>0.0727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1122.9409</td>\n",
       "      <td>9.852564e+06</td>\n",
       "      <td>3138.8795</td>\n",
       "      <td>0.9245</td>\n",
       "      <td>0.1102</td>\n",
       "      <td>0.0758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>911.3452</td>\n",
       "      <td>2.803663e+06</td>\n",
       "      <td>1674.4141</td>\n",
       "      <td>0.9743</td>\n",
       "      <td>0.0988</td>\n",
       "      <td>0.0729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1002.5575</td>\n",
       "      <td>3.926739e+06</td>\n",
       "      <td>1981.6002</td>\n",
       "      <td>0.9567</td>\n",
       "      <td>0.1049</td>\n",
       "      <td>0.0772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1167.8154</td>\n",
       "      <td>9.751516e+06</td>\n",
       "      <td>3122.7418</td>\n",
       "      <td>0.8784</td>\n",
       "      <td>0.1226</td>\n",
       "      <td>0.0876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1047.7778</td>\n",
       "      <td>7.833771e+06</td>\n",
       "      <td>2798.8874</td>\n",
       "      <td>0.9331</td>\n",
       "      <td>0.1128</td>\n",
       "      <td>0.0791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1010.0816</td>\n",
       "      <td>3.989282e+06</td>\n",
       "      <td>1997.3188</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.1106</td>\n",
       "      <td>0.0803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>846.8085</td>\n",
       "      <td>2.182535e+06</td>\n",
       "      <td>1477.3405</td>\n",
       "      <td>0.9724</td>\n",
       "      <td>0.0933</td>\n",
       "      <td>0.0709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1001.8451</td>\n",
       "      <td>4.904945e+06</td>\n",
       "      <td>2214.7111</td>\n",
       "      <td>0.9518</td>\n",
       "      <td>0.1053</td>\n",
       "      <td>0.0734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1060.8742</td>\n",
       "      <td>5.354348e+06</td>\n",
       "      <td>2313.9463</td>\n",
       "      <td>0.9490</td>\n",
       "      <td>0.1230</td>\n",
       "      <td>0.0847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>1003.1237</td>\n",
       "      <td>5.305620e+06</td>\n",
       "      <td>2228.7271</td>\n",
       "      <td>0.9476</td>\n",
       "      <td>0.1083</td>\n",
       "      <td>0.0775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>100.2165</td>\n",
       "      <td>2.734195e+06</td>\n",
       "      <td>581.7181</td>\n",
       "      <td>0.0280</td>\n",
       "      <td>0.0091</td>\n",
       "      <td>0.0052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            MAE           MSE       RMSE      R2   RMSLE    MAPE\n",
       "0      859.1907  2.456840e+06  1567.4310  0.9730  0.1016  0.0727\n",
       "1     1122.9409  9.852564e+06  3138.8795  0.9245  0.1102  0.0758\n",
       "2      911.3452  2.803663e+06  1674.4141  0.9743  0.0988  0.0729\n",
       "3     1002.5575  3.926739e+06  1981.6002  0.9567  0.1049  0.0772\n",
       "4     1167.8154  9.751516e+06  3122.7418  0.8784  0.1226  0.0876\n",
       "5     1047.7778  7.833771e+06  2798.8874  0.9331  0.1128  0.0791\n",
       "6     1010.0816  3.989282e+06  1997.3188  0.9625  0.1106  0.0803\n",
       "7      846.8085  2.182535e+06  1477.3405  0.9724  0.0933  0.0709\n",
       "8     1001.8451  4.904945e+06  2214.7111  0.9518  0.1053  0.0734\n",
       "9     1060.8742  5.354348e+06  2313.9463  0.9490  0.1230  0.0847\n",
       "Mean  1003.1237  5.305620e+06  2228.7271  0.9476  0.1083  0.0775\n",
       "SD     100.2165  2.734195e+06   581.7181  0.0280  0.0091  0.0052"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt = create_model('dt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NsOBIl8szZs1"
   },
   "source": [
    "Notice that the Mean score of all models matches with the score printed in `compare_models()`. This is because the metrics printed in the `compare_models()` score grid are the average scores across all CV folds. Similar to `compare_models()`, if you want to change the fold parameter from the default value of 10 to a different value then you can use the `fold` parameter. For Example: `create_model('dt', fold = 5)` to create Decision Tree using 5 fold cross validation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8RZB8YllzZs7"
   },
   "source": [
    "# 9.0 Tune a Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AYYWC1X5zZs-"
   },
   "source": [
    "When a model is created using the `create_model()` function it uses the default hyperparameters. In order to tune hyperparameters, the `tune_model()` function is used. This function automatically tunes the hyperparameters of a model on a pre-defined search space and scores it using k-fold cross validation. The output prints a score grid that shows MAE, MSE, RMSE, R2, RMSLE and MAPE by fold. <br/>\n",
    "<br/>\n",
    "**Note:** `tune_model()` does not take a trained model object as an input. It instead requires a model name to be passed as an abbreviated string similar to how it is passed in `create_model()`. All other functions in`pycaret.regression` require a trained model object as an argument."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5uUSmZLGzZtB"
   },
   "source": [
    "### 9.1 AdaBoost Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:25:23.226986Z",
     "start_time": "2020-04-23T13:25:17.516114Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "id": "XM7qgcGIzZtE",
    "outputId": "e87c7fac-4dfe-4733-ddc5-d44359b0ea0d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2755.4687</td>\n",
       "      <td>1.641891e+07</td>\n",
       "      <td>4052.0257</td>\n",
       "      <td>0.8197</td>\n",
       "      <td>0.2801</td>\n",
       "      <td>0.2603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2865.2921</td>\n",
       "      <td>2.326518e+07</td>\n",
       "      <td>4823.3988</td>\n",
       "      <td>0.8217</td>\n",
       "      <td>0.3017</td>\n",
       "      <td>0.2827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2570.6452</td>\n",
       "      <td>1.554483e+07</td>\n",
       "      <td>3942.6927</td>\n",
       "      <td>0.8574</td>\n",
       "      <td>0.2606</td>\n",
       "      <td>0.2355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2630.2358</td>\n",
       "      <td>1.475497e+07</td>\n",
       "      <td>3841.2197</td>\n",
       "      <td>0.8372</td>\n",
       "      <td>0.2719</td>\n",
       "      <td>0.2329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2422.9553</td>\n",
       "      <td>1.388942e+07</td>\n",
       "      <td>3726.8506</td>\n",
       "      <td>0.8268</td>\n",
       "      <td>0.2656</td>\n",
       "      <td>0.2252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2530.4172</td>\n",
       "      <td>1.962855e+07</td>\n",
       "      <td>4430.4123</td>\n",
       "      <td>0.8325</td>\n",
       "      <td>0.2635</td>\n",
       "      <td>0.2265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2744.5501</td>\n",
       "      <td>1.711563e+07</td>\n",
       "      <td>4137.1039</td>\n",
       "      <td>0.8392</td>\n",
       "      <td>0.2936</td>\n",
       "      <td>0.2696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2820.4718</td>\n",
       "      <td>1.767699e+07</td>\n",
       "      <td>4204.4014</td>\n",
       "      <td>0.7765</td>\n",
       "      <td>0.2999</td>\n",
       "      <td>0.2817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2574.6180</td>\n",
       "      <td>1.489709e+07</td>\n",
       "      <td>3859.6744</td>\n",
       "      <td>0.8537</td>\n",
       "      <td>0.2954</td>\n",
       "      <td>0.2708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2392.6510</td>\n",
       "      <td>1.631377e+07</td>\n",
       "      <td>4039.0306</td>\n",
       "      <td>0.8447</td>\n",
       "      <td>0.2632</td>\n",
       "      <td>0.2225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>2630.7305</td>\n",
       "      <td>1.695053e+07</td>\n",
       "      <td>4105.6810</td>\n",
       "      <td>0.8309</td>\n",
       "      <td>0.2796</td>\n",
       "      <td>0.2508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>153.6911</td>\n",
       "      <td>2.620557e+06</td>\n",
       "      <td>306.4572</td>\n",
       "      <td>0.0217</td>\n",
       "      <td>0.0158</td>\n",
       "      <td>0.0233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            MAE           MSE       RMSE      R2   RMSLE    MAPE\n",
       "0     2755.4687  1.641891e+07  4052.0257  0.8197  0.2801  0.2603\n",
       "1     2865.2921  2.326518e+07  4823.3988  0.8217  0.3017  0.2827\n",
       "2     2570.6452  1.554483e+07  3942.6927  0.8574  0.2606  0.2355\n",
       "3     2630.2358  1.475497e+07  3841.2197  0.8372  0.2719  0.2329\n",
       "4     2422.9553  1.388942e+07  3726.8506  0.8268  0.2656  0.2252\n",
       "5     2530.4172  1.962855e+07  4430.4123  0.8325  0.2635  0.2265\n",
       "6     2744.5501  1.711563e+07  4137.1039  0.8392  0.2936  0.2696\n",
       "7     2820.4718  1.767699e+07  4204.4014  0.7765  0.2999  0.2817\n",
       "8     2574.6180  1.489709e+07  3859.6744  0.8537  0.2954  0.2708\n",
       "9     2392.6510  1.631377e+07  4039.0306  0.8447  0.2632  0.2225\n",
       "Mean  2630.7305  1.695053e+07  4105.6810  0.8309  0.2796  0.2508\n",
       "SD     153.6911  2.620557e+06   306.4572  0.0217  0.0158  0.0233"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tuned_ada = tune_model('ada')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:25:31.211932Z",
     "start_time": "2020-04-23T13:25:31.208661Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "Ul0HJFoRzZtU",
    "outputId": "5ac4ab0b-c746-4a9f-a286-3e5cd2bbe536"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoostRegressor(base_estimator=None, learning_rate=0.11, loss='exponential',\n",
      "                  n_estimators=90, random_state=123)\n"
     ]
    }
   ],
   "source": [
    "#tuned model object is stored in the variable 'tuned_ada'. \n",
    "print(tuned_ada)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3kvdvfdUzZtj"
   },
   "source": [
    "### 9.2 Light Gradient Boosting Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:25:35.696328Z",
     "start_time": "2020-04-23T13:25:33.725433Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "id": "s1agvmDFzZtm",
    "outputId": "7cfab1a7-e7c2-40df-ad1e-a40067bcc4e0"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>696.8135</td>\n",
       "      <td>1.223186e+06</td>\n",
       "      <td>1105.9776</td>\n",
       "      <td>0.9866</td>\n",
       "      <td>0.0816</td>\n",
       "      <td>0.0611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>769.7567</td>\n",
       "      <td>2.878033e+06</td>\n",
       "      <td>1696.4767</td>\n",
       "      <td>0.9779</td>\n",
       "      <td>0.0740</td>\n",
       "      <td>0.0569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>796.1051</td>\n",
       "      <td>2.123431e+06</td>\n",
       "      <td>1457.1999</td>\n",
       "      <td>0.9805</td>\n",
       "      <td>0.0816</td>\n",
       "      <td>0.0629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>766.5171</td>\n",
       "      <td>1.927426e+06</td>\n",
       "      <td>1388.3177</td>\n",
       "      <td>0.9787</td>\n",
       "      <td>0.0815</td>\n",
       "      <td>0.0616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>740.2536</td>\n",
       "      <td>2.694766e+06</td>\n",
       "      <td>1641.5743</td>\n",
       "      <td>0.9664</td>\n",
       "      <td>0.0815</td>\n",
       "      <td>0.0601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>789.1642</td>\n",
       "      <td>4.094534e+06</td>\n",
       "      <td>2023.4955</td>\n",
       "      <td>0.9651</td>\n",
       "      <td>0.0805</td>\n",
       "      <td>0.0601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>720.8130</td>\n",
       "      <td>1.581897e+06</td>\n",
       "      <td>1257.7347</td>\n",
       "      <td>0.9851</td>\n",
       "      <td>0.0801</td>\n",
       "      <td>0.0603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>841.3175</td>\n",
       "      <td>3.026922e+06</td>\n",
       "      <td>1739.8053</td>\n",
       "      <td>0.9617</td>\n",
       "      <td>0.0857</td>\n",
       "      <td>0.0649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>816.6643</td>\n",
       "      <td>2.177376e+06</td>\n",
       "      <td>1475.5935</td>\n",
       "      <td>0.9786</td>\n",
       "      <td>0.0809</td>\n",
       "      <td>0.0628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>765.3249</td>\n",
       "      <td>2.960101e+06</td>\n",
       "      <td>1720.4943</td>\n",
       "      <td>0.9718</td>\n",
       "      <td>0.0933</td>\n",
       "      <td>0.0633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>770.2730</td>\n",
       "      <td>2.468767e+06</td>\n",
       "      <td>1550.6669</td>\n",
       "      <td>0.9753</td>\n",
       "      <td>0.0821</td>\n",
       "      <td>0.0614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>41.2386</td>\n",
       "      <td>7.905609e+05</td>\n",
       "      <td>253.3759</td>\n",
       "      <td>0.0081</td>\n",
       "      <td>0.0046</td>\n",
       "      <td>0.0021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           MAE           MSE       RMSE      R2   RMSLE    MAPE\n",
       "0     696.8135  1.223186e+06  1105.9776  0.9866  0.0816  0.0611\n",
       "1     769.7567  2.878033e+06  1696.4767  0.9779  0.0740  0.0569\n",
       "2     796.1051  2.123431e+06  1457.1999  0.9805  0.0816  0.0629\n",
       "3     766.5171  1.927426e+06  1388.3177  0.9787  0.0815  0.0616\n",
       "4     740.2536  2.694766e+06  1641.5743  0.9664  0.0815  0.0601\n",
       "5     789.1642  4.094534e+06  2023.4955  0.9651  0.0805  0.0601\n",
       "6     720.8130  1.581897e+06  1257.7347  0.9851  0.0801  0.0603\n",
       "7     841.3175  3.026922e+06  1739.8053  0.9617  0.0857  0.0649\n",
       "8     816.6643  2.177376e+06  1475.5935  0.9786  0.0809  0.0628\n",
       "9     765.3249  2.960101e+06  1720.4943  0.9718  0.0933  0.0633\n",
       "Mean  770.2730  2.468767e+06  1550.6669  0.9753  0.0821  0.0614\n",
       "SD     41.2386  7.905609e+05   253.3759  0.0081  0.0046  0.0021"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tuned_lightgbm = tune_model('lightgbm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ovz73MkgzZtx"
   },
   "source": [
    "### 9.3 Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:25:38.913881Z",
     "start_time": "2020-04-23T13:25:38.267918Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "id": "kFImcOpXzZt0",
    "outputId": "36165e01-0b2e-4fa4-efcc-5efa70a3236f"
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>874.4352</td>\n",
       "      <td>2.276756e+06</td>\n",
       "      <td>1508.8923</td>\n",
       "      <td>0.9750</td>\n",
       "      <td>0.0973</td>\n",
       "      <td>0.0740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>961.9344</td>\n",
       "      <td>5.311160e+06</td>\n",
       "      <td>2304.5955</td>\n",
       "      <td>0.9593</td>\n",
       "      <td>0.0947</td>\n",
       "      <td>0.0709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>867.5361</td>\n",
       "      <td>2.470714e+06</td>\n",
       "      <td>1571.8506</td>\n",
       "      <td>0.9773</td>\n",
       "      <td>0.0947</td>\n",
       "      <td>0.0732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>996.1490</td>\n",
       "      <td>4.213594e+06</td>\n",
       "      <td>2052.7042</td>\n",
       "      <td>0.9535</td>\n",
       "      <td>0.1034</td>\n",
       "      <td>0.0747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1084.2670</td>\n",
       "      <td>8.813849e+06</td>\n",
       "      <td>2968.8127</td>\n",
       "      <td>0.8901</td>\n",
       "      <td>0.1149</td>\n",
       "      <td>0.0816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1062.5522</td>\n",
       "      <td>8.153284e+06</td>\n",
       "      <td>2855.3956</td>\n",
       "      <td>0.9304</td>\n",
       "      <td>0.1131</td>\n",
       "      <td>0.0780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>911.7975</td>\n",
       "      <td>3.759584e+06</td>\n",
       "      <td>1938.9647</td>\n",
       "      <td>0.9647</td>\n",
       "      <td>0.0957</td>\n",
       "      <td>0.0693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>975.3512</td>\n",
       "      <td>4.392592e+06</td>\n",
       "      <td>2095.8512</td>\n",
       "      <td>0.9445</td>\n",
       "      <td>0.1061</td>\n",
       "      <td>0.0763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1053.7695</td>\n",
       "      <td>4.874987e+06</td>\n",
       "      <td>2207.9373</td>\n",
       "      <td>0.9521</td>\n",
       "      <td>0.1070</td>\n",
       "      <td>0.0802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>975.3018</td>\n",
       "      <td>4.146015e+06</td>\n",
       "      <td>2036.1766</td>\n",
       "      <td>0.9605</td>\n",
       "      <td>0.1176</td>\n",
       "      <td>0.0821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>976.3094</td>\n",
       "      <td>4.841254e+06</td>\n",
       "      <td>2154.1181</td>\n",
       "      <td>0.9507</td>\n",
       "      <td>0.1045</td>\n",
       "      <td>0.0760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>72.1572</td>\n",
       "      <td>2.035135e+06</td>\n",
       "      <td>448.3626</td>\n",
       "      <td>0.0241</td>\n",
       "      <td>0.0083</td>\n",
       "      <td>0.0042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            MAE           MSE       RMSE      R2   RMSLE    MAPE\n",
       "0      874.4352  2.276756e+06  1508.8923  0.9750  0.0973  0.0740\n",
       "1      961.9344  5.311160e+06  2304.5955  0.9593  0.0947  0.0709\n",
       "2      867.5361  2.470714e+06  1571.8506  0.9773  0.0947  0.0732\n",
       "3      996.1490  4.213594e+06  2052.7042  0.9535  0.1034  0.0747\n",
       "4     1084.2670  8.813849e+06  2968.8127  0.8901  0.1149  0.0816\n",
       "5     1062.5522  8.153284e+06  2855.3956  0.9304  0.1131  0.0780\n",
       "6      911.7975  3.759584e+06  1938.9647  0.9647  0.0957  0.0693\n",
       "7      975.3512  4.392592e+06  2095.8512  0.9445  0.1061  0.0763\n",
       "8     1053.7695  4.874987e+06  2207.9373  0.9521  0.1070  0.0802\n",
       "9      975.3018  4.146015e+06  2036.1766  0.9605  0.1176  0.0821\n",
       "Mean   976.3094  4.841254e+06  2154.1181  0.9507  0.1045  0.0760\n",
       "SD      72.1572  2.035135e+06   448.3626  0.0241  0.0083  0.0042"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tuned_dt = tune_model('dt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TcSjA7SUzZt-"
   },
   "source": [
    "`tune_model()` function is a random grid search of hyperparameters over a pre-defined search space. By default, it is set to optimize `R2` but this can be changed using the `optimize` parameter. For example: `tune_model('dt', optimize = 'MAE')` will search for the hyperparameters of a Decision Tree that result in the lowest `MAE` (lower is better). For the purposes of this example, we have used the default metric `R2` for the sake of simplicity only. The methodology behind selecting the right metric to evaluate a regressor is beyond the scope of this tutorial but if you would like to learn more about it, you can __[click here](https://www.dataquest.io/blog/understanding-regression-error-metrics/)__ to develop an understanding on regression error metrics. \n",
    "\n",
    "Notice how the results after tuning have been improved:\n",
    "\n",
    "- AdaBoost Regressor (Before: **`0.7467`** , After: **`0.8309`**) \n",
    "- Light Gradient Boosting Machine (Before: **`0.9711`** , After: **`0.9753`**)\n",
    "- Decision Tree (Before: **`0.9476`** , After: **`0.9507`**) \n",
    "\n",
    "Metrics alone are not the only criteria you should consider when finalizing the best model for production. Other factors to consider include training time, standard deviation of k-folds etc. As you progress through the tutorial series we will discuss those factors in detail at the intermediate and expert levels. For now, let's move forward considering the Tuned Light Gradient Boosting Machine stored in the `tuned_lightgbm` variable as our best model for the remainder of this tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HR-mHgtCzZuE"
   },
   "source": [
    "# 10.0 Plot a Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "N6i7Ggg_zZuH"
   },
   "source": [
    "Before model finalization, the `plot_model()` function can be used to analyze the performance across different aspects such as Residuals Plot, Prediction Error, Feature Importance etc. This function takes a trained model object and returns a plot based on the test / hold-out set. \n",
    "\n",
    "There are over 10 plots available, please see the `plot_model()` docstring for the list of available plots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HJCYRQj9zZuU"
   },
   "source": [
    "### 10.1 Residual Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:29:47.857901Z",
     "start_time": "2020-04-23T13:29:47.261824Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "colab_type": "code",
    "id": "ml-qe8dTzZuX",
    "outputId": "4c69b0b8-8e82-4003-f0e2-158b19a79f34"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(tuned_lightgbm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rM9dWgfVzZuh"
   },
   "source": [
    "### 10.2 Prediction Error Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:29:53.402735Z",
     "start_time": "2020-04-23T13:29:53.197809Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "GPwWRYehzZuk",
    "outputId": "e3dfe255-fa08-42f7-e556-74c7909f6e6e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(tuned_lightgbm, plot = 'error')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dWu_EtTGzZuu"
   },
   "source": [
    "### 10.3 Feature Importance Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:29:57.854731Z",
     "start_time": "2020-04-23T13:29:57.758312Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "7Yh852PPzZux",
    "outputId": "38295169-5000-4a07-e71d-76de9877ab42"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(tuned_lightgbm, plot='feature')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qI_tk-8RzZu8"
   },
   "source": [
    "*Another* way to analyze the performance of models is to use the `evaluate_model()` function which displays a user interface for all of the available plots for a given model. It internally uses the `plot_model()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:30:17.357283Z",
     "start_time": "2020-04-23T13:30:17.326916Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 398,
     "referenced_widgets": [
      "2f905c9057e849cfb68d8b4a73a9ae2c",
      "4705ae97c5c341db8a7efcc7e4334c79",
      "202f5f0dfbea4f6cb6a75e4aebd370c7",
      "4c0b5f15356140e9bd6ed3a2e0850baa",
      "63e7b6e04e3d43118698abaca0973960",
      "b7731479f5f141289a4939cb73adfb28",
      "5eb7949ac4a140118e9ee6be49921284",
      "19ea86ac89a349e281011fbd327c29c0",
      "c9bbc67e75d1477e8cd7a64502b98704",
      "5334e09f4bed4a0ba45635300e76027e",
      "14244d0fca4f40a8b3552675869748a3",
      "baf068abf86f44a9a9502f79dfe17eb5",
      "3495c4e24f1d4b22af1117ea2403a4fa",
      "83a10da0205742d9a383671a0be02606",
      "4ae774bce02d4c1ab1351cd75b25cf17",
      "ee5416de1bb1462e87b2fa6f9065d646",
      "36a9c708b65e498586a07d526e6a5d06",
      "aeb87dce945f4d588807575e9f6e64c3"
     ]
    },
    "colab_type": "code",
    "id": "J4ryBACHzZu_",
    "outputId": "246ab553-8043-4cbe-fe99-7ea00bb13aed"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Parameters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>boosting_type</th>\n",
       "      <td>gbdt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class_weight</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>colsample_bytree</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>importance_type</th>\n",
       "      <td>split</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>learning_rate</th>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_depth</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min_child_samples</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min_child_weight</th>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min_split_gain</th>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_estimators</th>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_jobs</th>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_leaves</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>objective</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random_state</th>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg_alpha</th>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reg_lambda</th>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>silent</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subsample</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subsample_for_bin</th>\n",
       "      <td>200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subsample_freq</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Parameters\n",
       "boosting_type           gbdt\n",
       "class_weight            None\n",
       "colsample_bytree           1\n",
       "importance_type        split\n",
       "learning_rate            0.4\n",
       "max_depth                 10\n",
       "min_child_samples         20\n",
       "min_child_weight       0.001\n",
       "min_split_gain           0.9\n",
       "n_estimators              90\n",
       "n_jobs                    -1\n",
       "num_leaves                10\n",
       "objective               None\n",
       "random_state             123\n",
       "reg_alpha                0.9\n",
       "reg_lambda               0.2\n",
       "silent                  True\n",
       "subsample                  1\n",
       "subsample_for_bin     200000\n",
       "subsample_freq             0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_model(tuned_lightgbm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CxKARgKAzZvJ"
   },
   "source": [
    "# 11.0 Predict on Test / Hold-out Sample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "r8k_rmplzZvL"
   },
   "source": [
    "Before finalizing the model, it is advisable to perform one final check by predicting the test/hold-out set and reviewing the evaluation metrics. If you look at the information grid in Section 6 above, you will see that 30% (1621 samples) of the data has been separated out as a test/hold-out sample. All of the evaluation metrics we have seen above are cross-validated results based on training set (70%) only. Now, using our final trained model stored in the `tuned_lightgbm` variable we will predict the hold-out sample and evaluate the metrics to see if they are materially different than the CV results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:31:47.571061Z",
     "start_time": "2020-04-23T13:31:47.543390Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "ozTyeSjCzZvY",
    "outputId": "1cdf25f4-0988-40fa-c8e9-5695fec11f05"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Light Gradient Boosting Machine</td>\n",
       "      <td>789.8498</td>\n",
       "      <td>2.985032e+06</td>\n",
       "      <td>1727.7244</td>\n",
       "      <td>0.9728</td>\n",
       "      <td>0.0841</td>\n",
       "      <td>0.0613</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Model       MAE           MSE       RMSE      R2  \\\n",
       "0  Light Gradient Boosting Machine  789.8498  2.985032e+06  1727.7244  0.9728   \n",
       "\n",
       "    RMSLE    MAPE  \n",
       "0  0.0841  0.0613  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict_model(tuned_lightgbm);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UouOXHaxzZvo"
   },
   "source": [
    "The R2 on the test/hold-out set is **`0.9728`** compared to **`0.9753`** achieved on `tuned_lightgbm` CV results (in section 9.2 above). This is not a significant difference. If there is a large variation between the test/hold-out and CV results, then this would normally indicate over-fitting but could also be due to several other factors and would require further investigation. In this case, we will move forward with finalizing the model and predicting on unseen data (the 10% that we had separated in the beginning and never exposed to PyCaret).\n",
    "\n",
    "(TIP : It's always good to look at the standard deviation of CV results when using `create_model()`.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "J0PmhEQFzZvr"
   },
   "source": [
    "# 12.0 Finalize Model for Deployment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Rtaj0uWgzZvx"
   },
   "source": [
    "Model finalization is the last step in the experiment. A normal machine learning workflow in PyCaret starts with `setup()`, followed by comparing all models using `compare_models()` and shortlisting a few candidate models (based on the metric of interest) to perform several modeling techniques such as hyperparameter tuning, ensembling, stacking etc. This workflow will eventually lead you to the best model for use in making predictions on new and unseen data. The `finalize_model()` function fits the model onto the complete dataset including the test/hold-out sample (30% in this case). The purpose of this function is to train the model on the complete dataset before it is deployed in production."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:33:51.300384Z",
     "start_time": "2020-04-23T13:33:51.215142Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "UPk310pezZv0"
   },
   "outputs": [],
   "source": [
    "final_lightgbm = finalize_model(tuned_lightgbm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:33:52.258663Z",
     "start_time": "2020-04-23T13:33:52.255298Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "IGFUGDAPzZwF",
    "outputId": "ddaa33c4-f728-444a-fc33-47da5c37304a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
      "              importance_type='split', learning_rate=0.4, max_depth=10,\n",
      "              min_child_samples=20, min_child_weight=0.001, min_split_gain=0.9,\n",
      "              n_estimators=90, n_jobs=-1, num_leaves=10, objective=None,\n",
      "              random_state=123, reg_alpha=0.9, reg_lambda=0.2, silent=True,\n",
      "              subsample=1.0, subsample_for_bin=200000, subsample_freq=0)\n"
     ]
    }
   ],
   "source": [
    "#Final Light Gradient Boosting Machine parameters for deployment\n",
    "print(final_lightgbm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QmJjaIkrzZwQ"
   },
   "source": [
    "**Caution:** One final word of caution. Once the model is finalized using `finalize_model()`, the entire dataset including the test/hold-out set is used for training. As such, if the model is used for predictions on the hold-out set after `finalize_model()` is used, the information grid printed will be misleading as you are trying to predict on the same data that was used for modeling. In order to demonstrate this point only, we will use `final_knn` under `predict_model()` to compare the information grid with the one above in section 11. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:34:02.730481Z",
     "start_time": "2020-04-23T13:34:02.704786Z"
    },
    "colab": {},
    "colab_type": "code",
    "id": "bmYJRTAyzZwU",
    "outputId": "58e2f57b-abda-4166-c82b-af52ab6f18b8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Light Gradient Boosting Machine</td>\n",
       "      <td>582.5217</td>\n",
       "      <td>1221486.605</td>\n",
       "      <td>1105.2089</td>\n",
       "      <td>0.9889</td>\n",
       "      <td>0.0653</td>\n",
       "      <td>0.0493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Model       MAE          MSE       RMSE      R2  \\\n",
       "0  Light Gradient Boosting Machine  582.5217  1221486.605  1105.2089  0.9889   \n",
       "\n",
       "    RMSLE    MAPE  \n",
       "0  0.0653  0.0493  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict_model(final_lightgbm);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5NkpL1ZHzZwr"
   },
   "source": [
    "Notice how the R2 in the `final_lightgbm` has increased to **`0.9889`** from **`0.9728`**, even though the model is same. This is because the `final_lightgbm` variable is trained on the complete dataset including the test/hold-out set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CgKSkSsZzZwv"
   },
   "source": [
    "# 13.0 Predict on Unseen Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6n7QFM94zZwy"
   },
   "source": [
    "The `predict_model()` function is also used to predict on the unseen dataset. The only difference from section 11 above is that this time we will pass the `data_unseen` parameter. `data_unseen` is the variable created at the beginning of the tutorial and contains 10% (600 samples) of the original dataset which was never exposed to PyCaret. (see section 5 for explanation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YdlpJUx0zZw4",
    "outputId": "5b45a2b5-c9a1-4d20-80f7-28211f07d586"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Carat Weight</th>\n",
       "      <th>Cut</th>\n",
       "      <th>Color</th>\n",
       "      <th>Clarity</th>\n",
       "      <th>Polish</th>\n",
       "      <th>Symmetry</th>\n",
       "      <th>Report</th>\n",
       "      <th>Price</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.21</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>G</td>\n",
       "      <td>VVS1</td>\n",
       "      <td>EX</td>\n",
       "      <td>EX</td>\n",
       "      <td>GIA</td>\n",
       "      <td>11572</td>\n",
       "      <td>10812.8943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.00</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>I</td>\n",
       "      <td>SI1</td>\n",
       "      <td>EX</td>\n",
       "      <td>VG</td>\n",
       "      <td>GIA</td>\n",
       "      <td>16775</td>\n",
       "      <td>15453.9273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.51</td>\n",
       "      <td>Good</td>\n",
       "      <td>F</td>\n",
       "      <td>SI1</td>\n",
       "      <td>VG</td>\n",
       "      <td>G</td>\n",
       "      <td>GIA</td>\n",
       "      <td>10429</td>\n",
       "      <td>10591.0952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.90</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>F</td>\n",
       "      <td>SI1</td>\n",
       "      <td>EX</td>\n",
       "      <td>EX</td>\n",
       "      <td>GIA</td>\n",
       "      <td>4523</td>\n",
       "      <td>4639.1068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.01</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>I</td>\n",
       "      <td>SI1</td>\n",
       "      <td>VG</td>\n",
       "      <td>VG</td>\n",
       "      <td>GIA</td>\n",
       "      <td>4375</td>\n",
       "      <td>4307.9166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Carat Weight        Cut Color Clarity Polish Symmetry Report  Price  \\\n",
       "0          1.21      Ideal     G    VVS1     EX       EX    GIA  11572   \n",
       "1          2.00      Ideal     I     SI1     EX       VG    GIA  16775   \n",
       "2          1.51       Good     F     SI1     VG        G    GIA  10429   \n",
       "3          0.90      Ideal     F     SI1     EX       EX    GIA   4523   \n",
       "4          1.01  Very Good     I     SI1     VG       VG    GIA   4375   \n",
       "\n",
       "        Label  \n",
       "0  10812.8943  \n",
       "1  15453.9273  \n",
       "2  10591.0952  \n",
       "3   4639.1068  \n",
       "4   4307.9166  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unseen_predictions = predict_model(final_lightgbm, data=data_unseen)\n",
    "unseen_predictions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wZnpuHoDzZxG"
   },
   "source": [
    "The `Label` column is added onto the `data_unseen` set. Label is the predicted value using the `final_lightgbm` model. If you want predictions to be rounded, you can use `round` parameter inside `predict_model()`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "os2dbiIrzZxJ"
   },
   "source": [
    "# 14.0 Saving the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "46CV19RlzZxL"
   },
   "source": [
    "We have now finished the experiment by finalizing the `tuned_lightgbm` model which is now stored in `final_lightgbm` variable. We have also used the model stored in `final_lightgbm` to predict `data_unseen`. This brings us to the end of our experiment, but one question is still to be asked: What happens when you have more new data to predict? Do you have to go through the entire experiment again? The answer is no, PyCaret's inbuilt function `save_model()` allows you to save the model along with entire transformation pipeline for later use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tXl6hkG9zZxN",
    "outputId": "4af19b19-6c0c-4b1e-a4f7-249e729091bd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Succesfully Saved\n"
     ]
    }
   ],
   "source": [
    "save_model(final_lightgbm,'Final Lightgbm Model 08Feb2020')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "q2wgdZ5ozZxX"
   },
   "source": [
    "(TIP : It's always good to use date in the filename when saving models, it's good for version control.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9LsyznpCzZxb"
   },
   "source": [
    "# 15.0 Loading the Saved Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7ZH-4EMLzZxd"
   },
   "source": [
    "To load a saved model at a future date in the same or an alternative environment, we would use PyCaret's `load_model()` function and then easily apply the saved model on new unseen data for prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2hsqdgn3zZxg",
    "outputId": "9db6b97b-ea93-452b-e4b4-121bea203bd3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Sucessfully Loaded\n"
     ]
    }
   ],
   "source": [
    "saved_final_lightgbm = load_model('Final Lightgbm Model 08Feb2020')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NBAXt62nzZx5"
   },
   "source": [
    "Once the model is loaded in the environment, you can simply use it to predict on any new data using the same `predict_model()` function. Below we have applied the loaded model to predict the same `data_unseen` that we used in section 13 above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "y7debJpCzZx8"
   },
   "outputs": [],
   "source": [
    "new_prediction = predict_model(saved_final_lightgbm, data=data_unseen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8hT1v3N0zZyD",
    "outputId": "c5f9994b-1859-49d0-9bb0-4d7f6c506b98"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Carat Weight</th>\n",
       "      <th>Cut</th>\n",
       "      <th>Color</th>\n",
       "      <th>Clarity</th>\n",
       "      <th>Polish</th>\n",
       "      <th>Symmetry</th>\n",
       "      <th>Report</th>\n",
       "      <th>Price</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.21</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>G</td>\n",
       "      <td>VVS1</td>\n",
       "      <td>EX</td>\n",
       "      <td>EX</td>\n",
       "      <td>GIA</td>\n",
       "      <td>11572</td>\n",
       "      <td>10812.8943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.00</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>I</td>\n",
       "      <td>SI1</td>\n",
       "      <td>EX</td>\n",
       "      <td>VG</td>\n",
       "      <td>GIA</td>\n",
       "      <td>16775</td>\n",
       "      <td>15453.9273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.51</td>\n",
       "      <td>Good</td>\n",
       "      <td>F</td>\n",
       "      <td>SI1</td>\n",
       "      <td>VG</td>\n",
       "      <td>G</td>\n",
       "      <td>GIA</td>\n",
       "      <td>10429</td>\n",
       "      <td>10591.0952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.90</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>F</td>\n",
       "      <td>SI1</td>\n",
       "      <td>EX</td>\n",
       "      <td>EX</td>\n",
       "      <td>GIA</td>\n",
       "      <td>4523</td>\n",
       "      <td>4639.1068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.01</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>I</td>\n",
       "      <td>SI1</td>\n",
       "      <td>VG</td>\n",
       "      <td>VG</td>\n",
       "      <td>GIA</td>\n",
       "      <td>4375</td>\n",
       "      <td>4307.9166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Carat Weight        Cut Color Clarity Polish Symmetry Report  Price  \\\n",
       "0          1.21      Ideal     G    VVS1     EX       EX    GIA  11572   \n",
       "1          2.00      Ideal     I     SI1     EX       VG    GIA  16775   \n",
       "2          1.51       Good     F     SI1     VG        G    GIA  10429   \n",
       "3          0.90      Ideal     F     SI1     EX       EX    GIA   4523   \n",
       "4          1.01  Very Good     I     SI1     VG       VG    GIA   4375   \n",
       "\n",
       "        Label  \n",
       "0  10812.8943  \n",
       "1  15453.9273  \n",
       "2  10591.0952  \n",
       "3   4639.1068  \n",
       "4   4307.9166  "
      ]
     },
     "execution_count": 27,
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   ],
   "source": [
    "new_prediction.head()"
   ]
  },
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   "source": [
    "Notice that the results of `unseen_predictions` and `new_prediction` are identical."
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "# 16.0 Wrap-up / Next Steps?"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
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    "id": "ct8oOjuvzZyS"
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   "source": [
    "This tutorial has covered the entire machine learning pipeline from data ingestion, pre-processing, training the model, hyperparameter tuning, prediction and saving the model for later use. We have completed all of these steps in less than 10 commands which are naturally constructed and very intuitive to remember such as `create_model()`, `tune_model()`, `compare_models()`. Re-creating the entire experiment without PyCaret would have taken well over 100 lines of code in most libraries.\n",
    "\n",
    "We have only covered the basics of `pycaret.regression`. In following tutorials we will go deeper into advanced pre-processing, ensembling, generalized stacking and other techniques that allow you to fully customize your machine learning pipeline and are must know for any data scientist.\n",
    "\n",
    "See you at the next tutorial. Follow the link to __[Regression Tutorial (REG102) - Level Intermediate](https://github.com/pycaret/pycaret/blob/master/Tutorials/Regression%20Tutorial%20Level%20Intermediate%20-%20REG102.ipynb)__"
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xHqY/gC3BpqZXgOh67efNfBF3SgqvtehJsj+I37KRtsSha2S4dDKSHGT36oSs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+W/+wMNqzYSWF9PzHaMm9e362u2Ri+lSneG8LqNPU9/TZNFD/6LI+/9WZev18/6tItBB4cZvG1l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F+Zz/6F3ofVAWv3D5jSx6+OmuYiLDpHbxp9wSHM1Vn73DWQ/+jjPvv52P7nuU1S/P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\ncJIIqlZ/jN16SUG03E1LgWMrZAdZqCkgZEjMSIR0j0GeR2K28sZ2HMVLD/6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        "metadata": {
         "tags": []
        },
        "output_type": "display_data",
        "text/plain": "<Figure size 576x396 with 2 Axes>"
       }
      ]
     }
    },
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     "model_module": "@jupyter-widgets/controls",
     "model_name": "IntProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
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      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "",
      "description": "Processing: ",
      "description_tooltip": null,
      "layout": "IPY_MODEL_baf068abf86f44a9a9502f79dfe17eb5",
      "max": 5,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_14244d0fca4f40a8b3552675869748a3",
      "value": 4
     }
    },
    "5eb7949ac4a140118e9ee6be49921284": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "IntProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
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      "_view_count": null,
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      "_view_name": "ProgressView",
      "bar_style": "",
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